Determining an Inventory Target for a Node of a Supply Chain

ABSTRACT

Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias.

CLAIM OF PRIORITY

This application is a continuation of Ser. No. 13/163,687, filed on Jun.18, 2011 and entitled “Determining an Inventory Target for a Node of aSupply Chain,” which is a continuation of U.S. Pat. No. 7,966,211, filedon Apr. 29, 2004 and entitled “Determining an Inventory Target for aNode of a Supply Chain,” which claims priority under 35 U.S.C. §119(e)to U.S. Provisional No. 60/470,068, filed on May 12, 2003 and entitled“Strategic Inventory Optimization.” U.S. patent application Ser. No.13/163,687, U.S. Pat. No. 7,966,211 and U.S. Provisional No. 60/470,068are commonly assigned to the assignee of the present application. Thedisclosure of related U.S. patent application Ser. No. 13/163,687, U.S.Pat. No. 7,966,211 and U.S. Provisional No. 60/470,068 are herebyincorporated by reference into the present disclosure as if fully setforth herein.

BACKGROUND

1. Technical Field of the Invention

This invention relates generally to the field of supply chain analysisand more specifically to determining an inventory target for a node of asupply chain.

2. Background of the Invention

A supply chain supplies a product to a customer, and may include nodesthat store inventory such as parts needed to produce the product. Aknown technique for determining the proper amount of inventory at eachnode may involve predicting the amount of inventory needed at the nodesto satisfy customer demand. Known techniques for determining the properamount of inventory, however, may not be able to accurately predict theamount of inventory needed at the nodes. It is generally desirable toaccurately predict the amount of inventory needed at the nodes.

SUMMARY OF THE INVENTION

In accordance with the present invention, disadvantages and problemsassociated with previous supply chain analysis techniques may be reducedor eliminated.

According to one embodiment of the present invention, determining aninventory target for a node of a supply chain includes calculating ademand stock for satisfying a demand over supply lead time at the nodeof the supply chain, and calculating a demand variability stock forsatisfying a demand variability of the demand over supply lead time atthe node. A demand bias of the demand at the node is established. Aninventory target for the node is determined based on the demand stockand the demand variability stock in accordance with the demand bias.

Certain embodiments of the invention may provide one or more technicaladvantages. For example, an inventory target may be determined from ademand stock and a demand variability stock. The demand stock coversmean demand over lead time, and the demand variability stock coversdemand variability over lead time. Using the demand stock and the demandvariability stock to determine an inventory target may provide for amore accuracy. Historical data may be used to determine the inventorytarget. The demand stock and the demand variability stock may be used toadjust parameters such as the supply lead time, the demand variability,or both to optimize the inventory target.

Certain embodiments of the invention may include none, some, or all ofthe above technical advantages. One or more other technical advantagesmay be readily apparent to one skilled in the art from the figures,descriptions, and claims included herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and itsfeatures and advantages, reference is made to the following description,taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an example system for determiningan inventory target for a node of a supply chain;

FIG. 2 is a diagram illustrating an example supply chain that receivessupplies from one or more suppliers and provides products to one or morecustomers;

FIG. 3 is a diagram illustrating an example node of the supply chain ofFIG. 2;

FIG. 4 is a graph illustrating a predicted demand and an actual demandwith respect to time;

FIG. 5 is a flowchart illustrating an example method for determining aninventory target for a node of a supply chain;

FIG. 6 is a flowchart illustrating an example method for determining aninventory target for a node of a supply chain in accordance withhistorical data; and

FIG. 7 is a flowchart illustrating an example method for optimizing aninventory target for a node of a supply chain.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example system 10 fordetermining an inventory target for a node of a supply chain thatsupplies products to customers in response to a demand. The demand maybe represented as a predicted demand, which may be expressed as a meandemand and a demand variability. System 10 may, for example, calculate ademand stock and a demand variability stock for a node to satisfy thepredicted demand. The demand stock covers mean demand over lead time,and the demand variability stock covers demand variability over leadtime. System 10 may estimate an inventory target from the demand stockand the demand variability stock. According to one embodiment, system 10may adjust the inventory target in response to historical data.According to another embodiment, system 10 may use the estimate of theinventory target to adjust parameters for the node such as the supplylead time, the demand variability, or both.

According to the illustrated embodiment, system 10 includes a clientsystem 20, a server system 24, and a database 26 coupled as shown inFIG. 1. Client system 20 allows a user to communicate with server system24 to optimize inventory in a supply chain. Server system 24 managesapplications for optimizing inventory in a supply chain. Database 26stores data that may be used by server system 24. According to theillustrated embodiment, server system 24 includes a processor 30 and oneor more engines 32 coupled as shown in FIG. 1. Processor 30 manages theoperation of server system 24, and may comprise any device operable toaccept input, process the input according to predefined rules, andproduce an output. According to the illustrated embodiment, engines 32includes a demand planning engine 36, an optimization engine 38, and asupply chain planning engine 40.

Demand planning engine 36 generates a demand forecast that predicts thedemand at the nodes of a supply chain. Optimization engine 38 optimizesthe inventory at the nodes of a supply chain, and may estimate aninventory target from a demand stock and a demand variability stock.Supply chain planning engine 40 generates a plan for a supply chain.According to one embodiment, demand planning engine 36, optimizationengine 38, and supply chain planning engine 40 may interact with eachother. As an example, demand planning engine 36 may provide a demandforecast to optimization engine 38. Optimization engine 38 may optimizethe inventory in accordance with the demand forecast in order togenerate inventory targets, which are provided to supply chain planningengine 40. Supply chain planning engine 40 may generate a supply planfor the supply chain in accordance with the inventory targets.

According to one embodiment, optimization engine 38 may provide demandplanning engine 36 and supply chain engine 40 with policy information.As an example, optimization engine may instruct demand planning engine36 to decrease the demand variability of the demand estimate. As anotherexample, optimization engine 38 may instruct supply chain planningengine 40 to decrease the supply lead time or supply lead timevariation.

Client system 20 and server system 24 may each operate on one or morecomputers at one or more locations and may include appropriate inputdevices, output devices, mass storage media, processors, memory, orother components for receiving, processing, storing, and communicatinginformation according to the operation of system 10. For example, thepresent invention contemplates the functions of both client system 20and server system 24 being provided using a single computer system, suchas a single personal computer. As used in this document, the term“computer” refers to any suitable device operable to accept input,process the input according to predefined rules, and produce output, forexample, a server, workstation, personal computer, network computer,wireless telephone, personal digital assistant, one or moremicroprocessors within these or other devices, or any other suitableprocessing device. Database 26 may include any suitable data storagearrangement and may operate on one or more computers at one or morelocations.

Client system 20, server system 24, and database 26 may be integrated orseparated according to particular needs. Client system 20, server system24, and database 26 may be coupled to each other using one or morecomputer buses, local area networks (LANs), metropolitan area networks(MANs), wide area networks (WANs), a global computer network such as theInternet, or any other appropriate wireline, optical, wireless, or otherlinks.

Modifications, additions, or omissions may be made to system 10 withoutdeparting from the scope of the invention. For example, system 10 mayhave more, fewer, or other modules. Moreover, the operations of system10 may be performed by more, fewer, or other modules. For example, theoperations of simulation engine 34 and optimization engine 38 may beperformed by one module, or the operations of optimization engine 38 maybe performed by more than one module. Additionally, functions may beperformed using any suitable logic comprising software, hardware, otherlogic, or any suitable combination of the preceding. As used in thisdocument, “each” refers to at least one member of a set.

FIG. 2 is a diagram illustrating an example supply chain 70 thatreceives supplies from one or more suppliers 80 and provides products toone or more customers 84. Items flow through supply chain 70, and may betransformed or remain the same as they flow through supply chain 70.Items may comprise, for example, parts or supplies that may be used togenerate the products. For example, an item may comprise a part of theproduct, or an item may comprise a supply that is used to manufacturethe product, but does not become a part of the product. Downstreamrefers to the direction from suppliers 80 to customers 84, and upstreamrefers to the direction from customers 84 to suppliers 80.

Supply chain 70 may include any suitable number of nodes 76 and anysuitable number of arcs 78 between nodes 76, configured in any suitablemanner. According to the illustrated embodiment, items from supplier 80flow to node 76 a, which sends items to node 76 b. Node 76 b sends itemsto node 76 c, which sends items to nodes 76 d and 76 e. Nodes 76 d and76 e provide products to customers 84 a and 84 b, respectively. A supplylead time for a node 76 refers to the time it takes for a supply to beprovided to the node 76 from an upstream node 76.

Although supply chain 70 is illustrated as having five nodes 76 a-e andfour arcs 78 a-d, modifications, additions, or omissions may be made tosupply chain 70 without departing from the scope of the invention. Forexample, supply chain 70 may have more or fewer nodes 76 or arcs 78.Moreover, nodes 76 or arcs 78 may have any suitable configuration. Forexample, node 76 a may supply items to node 76 c, but not to node 76 b.

Certain characteristics of supply chain 70 may make it difficult forsupply chain 70 to respond to a customer demand. For example, highdemand variability and long supply lead times may hinder theresponsiveness of supply chain 70. Redistributing inventory towardsdownstream nodes 76 of supply chain 70 may improve responsiveness.Distributing inventory towards downstream nodes 76, however, mayincrease the inventory cost and the risk of obsolete inventory.Accordingly, different nodes 76 of supply chain 70 may be selected asresponse buffers in order to balance the responsiveness and flexibilityof supply chain 70.

FIG. 3 is a diagram illustrating an example node 76 of supply chain 70of FIG. 2. A demand forecast may be generated for node 76. The demandforecast may predict a mean demand over supply lead time and a demandvariability. A demand stock and a demand variability stock may beestimated for the mean demand over supply lead time and the demandvariability. The demand stock of a node 76 represents the stockcalculated to cover the mean demand over the supply lead time at thenode 76. The demand variability stock for a node 76 represents the stockcalculated to cover the demand variability of the demand over the supplylead time at the node 76. Since the mean demand over supply lead time isdeterministic and the demand variability is probabilistic, the demandstock is deterministic and the variability stock is probabilistic.

For example, the supply lead time SLT for node 76 may be SLT=2 weeks,and the demand forecast may predict a mean demand d=1,000 units per weekwith a demand variability σ_(d)=10%. Optimization engine 38 maycalculate demand stock SD=d×SLT=(1,000 units/1 week)×2 weeks=2,000units. The demand variability stock SV may be calculated according toSD×σ_(d)=2,000 units×10%=200 units. The inventory target IT may beestimated from SD and SV according to IT=SD+SV=2,000 units+200units=2,200 units.

According to one embodiment, optimization engine 38 may calculate thedemand stock independently from the demand variability stock. Separatecalculations of the demand stock and the demand variability stock mayaid in identifying changes to a supply chain 70 that may be made. Forexample, if the demand stock is 85% of the target inventory, and thedemand variability stock is 15% of the total inventory, then a user maydetermine that decreasing the demand stock may be more beneficial thandecreasing the demand variability stock.

Separate calculations of the demand stock and the demand variabilitystock may also provide visibility on how changing certain parameterssuch as the supply lead time, supply lead time variability, or demandvariability affects optimization of inventory targets. For example,decreasing demand variability typically decreases the demand variabilitystock, which may allow for decreasing the inventory target, relaxingsupply lead time requirements, or both. As another example, decreasingthe supply lead time, supply lead time variability, or both typicallydecreases the demand stock, which may allow for decreasing the inventorytarget, relaxing demand variability requirements, or both.

FIG. 4 is a graph 90 illustrating a predicted demand 92 and an actualdemand 94 with respect to time. Predicted demand 92 represents a demandthat is calculated without knowledge of the actual demand, and may bedetermined from a demand forecast generated by demand planning engine36. Predicted demand 92 may include a mean demand d and a demandvariability σ_(d) with respect to time. Actual demand 94 represents theknown demand. In the illustrated example, actual demand 94 is greaterthan predicted demand 92.

Different business models may use different types of demand forecasts ormay not even use demand forecasts at all. Examples of business modelsinclude the build-to-forecast model, the assemble-to-order model, andthe build-to-order model. According to the build-to-forecast model,products are produced in response to a demand forecast.Build-to-forecast models typically require an accurate and precisedemand forecast. According to the assemble-to-order model, parts of theproduct may be produced, and then the product is assembled from theparts in response to an order. Assemble-to-order models typicallyrequire an accurate and precise demand forecast for the parts of theproduct. According to the build-to-order model, products are produced inresponse to an order from a customer rather than to a demand forecast.

FIG. 5 is a flowchart illustrating an example method for estimating aninventory target for a node 76 of supply chain 70. The method begins atstep 100, where optimization engine 38 receives a demand forecast and asupply lead time for node 76. Demand planning engine 36 may provide thedemand forecast, and supply chain planning engine 40 may provide thesupply lead time. The demand forecast may include a mean demand and ademand variability. According to one example, the supply lead time SLTmay be SLT=2 weeks. The mean demand and the demand variability areestablished from the demand forecast at step 104. According to oneexample, the demand forecast may predict a mean demand d=1,000 units perweek with a demand variability σ_(d)=10%.

The demand stock is determined at step 108. The demand stock mayrepresent the stock that covers the mean demand over a supply lead time.The demand stock SD may be calculated by multiplying the mean demand dper time unit by the supply lead time SLT. For example, SD=d×SLT=(1,000units/1 week)×2 weeks=2,000 units. The demand variability stock isdetermined at step 112. The demand variability stock SV may becalculated by multiplying demand stock SD by variability (σ_(d)according to SV=SD×σ_(d)=2,000 units×10%=200 units. The inventory targetis calculated at step 116. The inventory target may be calculated byadding the demand stock with the demand variability stock. For example,inventory target IT may be estimated from SD and SV according toIT=SD+SV=2,000 units+200 units=2,200 units. The reports are resulted atstep 120. After reporting the results, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. Additionally, steps may beperformed in any suitable order without departing from the scope of theinvention.

FIG. 6 is a flowchart illustrating an example method for estimatinginventory targets for supply chain 70 in accordance with historicaldata. The method begins at step 200, where optimization engine 38calculates an inventory target for a node 76 of supply chain 70. Theinventory target may be calculated according to the method describedwith reference to FIG. 5. For example, the inventory target IT may bedetermined from a demand stock SD and a demand variability stock SVaccording to IT=SD+SV. Predicted demand 92 is compared with the actualdemand 94 at step 204. An example of predicted demand 92 and actualdemand 94 is described with reference to FIG. 4.

Predicted demand 92 may exhibit a demand bias such as a positive biaswhen compared with actual demand 94. A demand bias refers to thetendency of predicted demand 92 to be greater than or less than actualdemand 94. A positive bias occurs when predicted demand 92 is less thanactual demand 94, and negative bias occurs when predicted demand 92 isgreater than actual demand 94. If there is no positive bias, the methodproceeds to step to 220.

If there is a positive bias, the method proceeds to step 212. Supplychain 70 may be associated with a forecast business model such as abuild-to-forecast or an assemble-to-order business model. If thebusiness model is not a forecast business model, the method proceeds tostep 220. If the business model is a forecast business model, the methodproceeds to step 216. At step 216, the inventory target is adjusted. Theinventory target IT may be adjusted by, for example, ignoring the demandvariability stock SV such that IT=SD. For a build-to-forecast businessmodel, if the forecast for a product is positive, then the demandvariability stock for the product might not be needed. For anassemble-to-order forecast, if the forecast for a part is positive, thenthe demand variability stock for the part might not be needed. Theresults are reported at step 220. After reporting the results, themethod ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. Additionally, steps may beperformed in any suitable order without departing from the scope of theinvention.

FIG. 7 is a flowchart illustrating an example method for optimizinginventory in supply chain 70. The method may be used to determine theeffect of changing a supply parameter, a demand parameter, or both oninventory optimization. A supply parameter refers to a parameterrelevant to the supply for a node 76 such as the supply lead time or thesupply lead time variability. A demand parameter refers to a parameterrelevant to the demand on a node 76 such as the mean demand or thedemand variability. The benefits of the response may be compared withthe cost of the change in order to adjust an inventory target. Themethod begins at step 300, where optimization engine 38 calculates aninventory target. The inventory target may be calculated according tothe method described with reference to FIG. 4. For example, theinventory target may be calculated by adding a demand stock to a demandvariability stock.

Steps 304 through 308 describe changing a supply parameter such as asupply lead time or a supply lead time variability and evaluating theeffects of the change. The supply parameter is changed at step 304. Thesupply parameter may be changed by, for example, decreasing the supplylead time. Changing a supply parameter however, typically has anassociated cost. For example, costs related to decreasing a supply leadtime may include an increase in delivery costs. The response to thechange is determined at step 306. The response may have an associatedbenefit. For example, decreasing the supply lead time may result in adecrease in the demand stock, which in turn results in a decrease in theinventory target. The cost of the change is compared to the benefit ofthe response at step 308. After comparing, the method proceeds to step320.

Step 314 through 318 describe changing the demand variability andevaluating the effects of the change. The demand variability is changedat step 314. For example, the demand variability may be decreased byimproving the precision of the demand forecast received from demandplanning engine 36. Changing the demand variability, however, mayinvolve certain costs. For example, costs related to decreasing thedemand variability may include the cost of purchasing software thatgenerates a more precise demand estimate or the cost of increased timeor data needed to produce a more precise demand estimate. The responseto the change is determined at step 316. The response may have anassociated benefit. For example, decreasing the demand variability maydecrease the demand variability stock, which in turn may decrease theinventory target. The cost of the change is compared to the benefit ofthe response to the change at step 318. After comparing, the methodproceeds to step 320.

The supply lead time, the demand variability, or both are adjusted inresponse to the comparisons at step 320. For example, if the benefit ofchanging the supply lead time outweighs the cost of changing the supplylead time, the supply lead time may be changed. As another example, ifthe benefit of changing the demand variability outweigh the cost ofchanging the demand variability, the demand variability may be changed.The results are reported at step 322. After reporting the results, themethod ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. For example, steps 304through 308 or steps 314 through 318 may be omitted. Additionally, stepsmay be performed in any suitable order without departing from the scopeof the invention. For example, steps 304 through 308 and steps 314through 318 may be preformed concurrently such that changing the supplylead time and the demand variability at steps 304 and 314 may bepreformed concurrently. The responses may be checked substantiallysimultaneously at step 306 and 316, and the costs and benefits may becompared substantially simultaneously at step 308 and 318.

Certain embodiments of the invention may provide one or more technicaladvantages. For example, an inventory target may be determined from ademand stock and a demand variability stock. The demand stock coversmean demand over lead time, and the demand variability stock coversdemand variability over lead time. Using the demand stock and the demandvariability stock to determine an inventory target may provide for amore accuracy. Historical data may be used to determine the inventorytarget. The demand stock and the demand variability stock may be used toadjust parameters such as the supply lead time, the demand variability,or both to optimize the inventory target.

Although an embodiment of the invention and its advantages are describedin detail, a person skilled in the art could make various alterations,additions, and omissions without departing from the spirit and scope ofthe present invention as defined by the appended claims.

What is claimed is:
 1. A computer-implemented method of estimatinginventory targets of a supply chain, comprising: calculating, by acomputer, an inventory target for a node of a supply chain; comparing,by the computer, a predicted demand to an actual demand, the predicteddemand exhibiting a demand bias that represents a tendency of thepredicted demand to be greater or less than the actual demand;determining, by the computer, whether the demand bias is a positive biasor a negative bias, the positive bias comprising the actual demandexceeding the predicted demand and the negative bias comprising thepredicted demand exceeding the negative demand; and adjusting, by thecomputer, the inventory target when the demand bias is the positivebias.
 2. The method of claim 1, wherein calculating the inventory targetcomprises: calculating, by the computer, a demand stock that satisfies ademand over supply lead time at a node of the supply chain; calculating,by the computer, a demand variability stock that satisfies a demandvariability of the demand over supply lead time at the node;establishing, by the computer, the demand bias of the demand at thenode; and determining, by the computer, the inventory target of the nodebased on the demand stock and the demand variability stock in accordancewith the demand bias.
 3. The method of claim 2, further comprisingadjusting the inventory target by ignoring the demand variable stocksuch that the inventory target equals the demand stock.
 4. The method ofclaim 1, further comprising calculating the inventory target by adding ademand stock to a demand variable stock.
 5. The method of claim 1,further comprising calculating the predicted demand by ignoring theactual demand.
 6. The method of claim 5, further comprising: generatinga demand forecast; and determining the predicted demand from the demandforecast.
 7. The method of claim 6, wherein the actual demand comprisesa known demand.
 8. A system for estimating inventory targets of a supplychain, comprising: an optimization engine tangibly embodied on acomputer-readable medium configured to calculate an inventory target fora node of a supply chain; and a computer system coupled to theoptimization engine and configured to: compare a predicted demand to anactual demand, the predicted demand exhibiting demand bias thatrepresents a tendency of the predicted demand to be greater or less thanthe actual demand; determine whether the demand bias is a positive biasor a negative bias, the positive bias comprising the actual demandexceeding the predicted demand and the negative bias comprising thepredicted demand exceeding the negative demand; and adjust the inventorytarget when the supply chain is associated with a forecast businessmodel and the demand bias is the positive bias.
 9. The system of claim8, wherein the computer system if further configured to: calculate ademand stock that satisfies a demand over supply lead time at a node ofthe supply chain; calculate a demand variability stock that satisfies ademand variability of the demand over supply lead time at the node;establish the demand bias of the demand at the node; and determine theinventory target of the node based on the demand stock and the demandvariability stock in accordance with the demand bias.
 10. The system ofclaim 9, wherein the computer system if further configured to adjust theinventory target by ignoring the demand variable stock such that theinventory target equals the demand stock.
 11. The system of claim 8,wherein the computer system if further configured to calculate theinventory target by adding a demand stock to a demand variable stock.12. The system of claim 8, wherein the computer system is furtherconfigured to calculate the predicted demand by ignoring the actualdemand.
 13. The system of claim 12, wherein the computer system isfurther configured to generate a demand forecast and determine thepredicted demand from the demand forecast.
 14. The system of claim 13,wherein the actual demand comprises a known demand.
 15. A non-transitorycomputer-readable medium embodied with software for estimating inventorytargets of a supply chain, the software when executed by a computer isconfigured to: calculate an inventory target for a node of a supplychain; compare a predicted demand to an actual demand, the predicteddemand exhibiting demand bias that represents a tendency of thepredicted demand to be greater or less than the actual demand; determinewhether the demand bias is a positive bias or a negative bias, thepositive bias comprising the actual demand exceeding the predicteddemand and the negative bias comprising the predicted demand exceedingthe negative demand; and adjust the inventory target when the demandbias is the positive bias.
 16. The non-transitory computer-readablemedium of claim 15, wherein calculate the inventory target comprises:calculate a demand stock that satisfies a demand over supply lead timeat a node of the supply chain; calculate a demand variability stock thatsatisfies a demand variability of the demand over supply lead time atthe node; establish the demand bias of the demand at the node; anddetermine the inventory target of the node based on the demand stock andthe demand variability stock in accordance with the demand bias.
 17. Thenon-transitory computer-readable medium of claim 15, wherein thesoftware is further configured to adjust the inventory target byignoring the demand variable stock such that the inventory target equalsthe demand stock.
 18. The non-transitory computer-readable medium ofclaim 15, wherein the software is further configured to calculate theinventory target by adding a demand stock to a demand variable stock.19. The non-transitory computer-readable medium of claim 18, wherein thesoftware is further configured to: calculate the predicted demand byignoring the actual demand; generate a demand forecast; and determinethe predicted demand from the demand forecast.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the actual demandcomprises a known demand.